%run visualizations.py

LOAD DATA

cities = pd.read_csv('./data/twitter_coverage_cities.csv')
countries = pd.read_csv('./data/twitter_coverage_countries.csv')

I. Users by Country

Plot that shows the total number of users per country jointly with the number of users per 1,000 people.

Arguments of this function:

• data (dataframe): dataframe grouped by country
• top_k (int): top-k number of countries to plot
• sort_by_gross_users (boolean): If True, it sorts the barchart by the gross number of users; if False,sorts the barchart by the relative (per 1,000 people) number of users
• log_scale (boolean): if True, it plots a log scale for the total number of users; if False, it plots a linear scale

top_countries_two_axis(data = countries,top_k= 30, sort_by_gross_users = True, log_scale = True)

II. Users by City

Plot that shows the total number of users per city jointly with the number of users per 1,000 people. Only cities with population larger than 300k were included

Arguments of this function:

• data (dataframe): dataframe grouped by country
• top_k (int): top-k number of cities to plot
• sort_by_gross_users (bool): If True, it sorts the barchart by the gross number of users; if False,sorts the barchart by the relative (per 1,000 people) number of users
• log_scale(bool): if True, it plots a log scale for the total number of users; if False, it plots a linear scale

top_cities_two_axis(data = cities,top_k= 30, sort_by_gross_users = True, log_scale = True)

III. Twitter users vs GDP per capita

Plot that shows the relationship between twitter user rate (users per 1,000 people) and GDP per capita (2019) per country (log scale). The only input is the countries dataframe

plot_gdp_and_users(countries)

IV. MAPS

A. Users by City (total number of users)

Each dot represents the size of the total number of users

Arguments of this function:

• dataframe (pandas dataframe): dataframe of cities
• total_users (bool): If True, the size of the dot represents the total number of users. If False, dots represent users per 1k people

dot_maps(cities,total_users = True)

Make this Notebook Trusted to load map: File -> Trust Notebook

B. Users by City (rate of users per thousand people)

Each dot represents the size of the rate of users per 1,000K

Arguments of this function:

dataframe (pandas dataframe): dataframe of cities total_users (bool): If True, the size of the dot represents the total number of users. If False, dots represent users per 1k people

dot_maps(cities,total_users = False)

Make this Notebook Trusted to load map: File -> Trust Notebook